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Journal articles on the topic 'Web metrics'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 September 2023

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1

Belanger, France, Weiguo Fan, L. Christian Schaupp, Anjala Krishen, Jeannine Everhart, David Poteet, and Kent Nakamoto. "Web site success metrics." Communications of the ACM 49, no. 12 (December 2006): 114–16. http://dx.doi.org/10.1145/1183236.1183256.

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2

Calero, Coral, Julián Ruiz, and Mario Piattini. "Classifying web metrics using the web quality model." Online Information Review 29, no. 3 (June 2005): 227–48. http://dx.doi.org/10.1108/14684520510607560.

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3

Dhyani, Devanshu, Wee Keong Ng, and Sourav S. Bhowmick. "A survey of Web metrics." ACM Computing Surveys 34, no. 4 (December 2002): 469–503. http://dx.doi.org/10.1145/592642.592645.

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4

Mendes, Emilia, Nile Mosley, and Steve Counsell. "Investigating Web size metrics for early Web cost estimation." Journal of Systems and Software 77, no. 2 (August 2005): 157–72. http://dx.doi.org/10.1016/j.jss.2004.08.034.

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5

Singh, Jaipuneet, and Simarpreet Singh. "Review of the Web Metrics Model." International Journal of Applied Research on Information Technology and Computing 4, no. 1 (2013): 55. http://dx.doi.org/10.5958/j.0975-8089.4.1.005.

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6

Chiang, I‐Ping, Chun‐Yao Huang, and Chien‐Wen Huang. "Traffic metrics and Web 2.0‐ness." Online Information Review 34, no. 1 (February 23, 2010): 115–26. http://dx.doi.org/10.1108/14684521011024155.

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7

Domènech, Josep, José A. Gil, Julio Sahuquillo, and Ana Pont. "Web prefetching performance metrics: A survey." Performance Evaluation 63, no. 9-10 (October 2006): 988–1004. http://dx.doi.org/10.1016/j.peva.2005.11.001.

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8

Hölttä, Vesa, and Heikki Koivo. "Performance metrics for web-forming processes." Journal of Process Control 21, no. 6 (July 2011): 885–92. http://dx.doi.org/10.1016/j.jprocont.2011.03.008.

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9

Marchetto, Alessandro, Roberto Tiella, Paolo Tonella, Nadia Alshahwan, and Mark Harman. "Crawlability metrics for automated web testing." International Journal on Software Tools for Technology Transfer 13, no. 2 (October 22, 2010): 131–49. http://dx.doi.org/10.1007/s10009-010-0177-3.

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10

Sathya, M., P. Dhavachelv, and G. Sureshkuma. "Web Service Categorization Using Structural Metrics." International Journal of Soft Computing 5, no. 4 (April 1, 2010): 164–70. http://dx.doi.org/10.3923/ijscomp.2010.164.170.

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11

Thelwall, Mike. "Web use and peer interconnectivity metrics for academic web sites." Journal of Information Science 29, no. 1 (February 2003): 1–10. http://dx.doi.org/10.1177/016555150302900101.

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12

Riza, Ahmad, Mohamad Alif Irfan Anshori, Farrah Arrazy, and M. Ainul Yaqin. "Pengukuran Metrik Kompleksitas Web Service Sekolah." Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) 5, no. 1 (March 1, 2020): 147. http://dx.doi.org/10.30645/jurasik.v5i1.179.

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The Complexity Metric Diagram is a method of measuring software complexity. This method uses Class Diagrams as test parameters. This study uses test data from previous research regarding the design of Information Systems accompanied by Class Diagrams with case studies of School Academic Information Systems. The facts found in the field that school software developers do not conduct application audits so it is not ripe to face problems that will arise in the future. The results of the calculation of the complexity metrics in the School Academic Information System produce an output of 993.32 units.

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13

Zahedi Nooghabi, Mahdi, and Akram Fathian Dastgerdi. "Proposed metrics for data accessibility in the context of linked open data." Program 50, no. 2 (April 4, 2016): 184–94. http://dx.doi.org/10.1108/prog-01-2015-0007.

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Purpose – One of the most important categories in linked open data (LOD) quality models is “data accessibility.” The purpose of this paper is to propose some metrics and indicators for assessing data accessibility in LOD and the semantic web context. Design/methodology/approach – In this paper, at first the authors consider some data quality and LOD quality models to review proposed subcategories for data accessibility dimension in related texts. Then, based on goal question metric (GQM) approach, the authors specify the project goals, main issues and some questions. Finally, the authors propose some metrics for assessing the data accessibility in the context of the semantic web. Findings – Based on GQM approach, the authors determined three main issues for data accessibility, including data availability, data performance, and data security policy. Then the authors created four main questions related to these issues. As a conclusion, the authors proposed 27 metrics for measuring these questions. Originality/value – Nowadays, one of the main challenges regarding data quality is the lack of agreement on widespread quality metrics and practical instruments for evaluating quality. Accessibility is an important aspect of data quality. However, few researches have been done to provide metrics and indicators for assessing data accessibility in the context of the semantic web. So, in this research, the authors consider the data accessibility dimension and propose a comparatively comprehensive set of metrics.

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14

Jayanthi, J., and M. Ezhilmathi. "Evaluating the Effectiveness of Web Search Metrics." Asian Journal of Computer Science and Technology 1, no. 2 (November 5, 2012): 16–19. http://dx.doi.org/10.51983/ajcst-2012.1.2.1707.

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Software metrics are the key performance indicators, using which the performance of a system can be assessed quantitatively. Metrics can also be applied for personalized web search which can be used to retrieve relevant results for each individual user depending on their unique profile. Although personalized search based on user profile has been under research for many years and various metrics have been proposed, it is still uncertain whether personalization is unswervingly effective on different queries for different user profiles. We present a framework for personalized search which retrieves result based on user profile. We maintain user profile in the form of Preference Network (PN). We further propose metrics for ranking the search results based on user profile

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15

Singh, Sukhjinder, Amit Makkar, and Navneet Singh. "A METRICS BASED APPROACH TO ANALYSE WEB USAGE PATTERN." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 1, no. 1 (December 30, 2011): 1–6. http://dx.doi.org/10.24297/ijct.v1i1.6089.

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Web metrics are established goals and standards for measuring website performance. Web Analytics can be used to analyze and statistically process user and customer behavior. Web Analytics especially refers to the use of data collected from a Website to determine which aspects of the Website work towards the business objectives. This paper provides a web metrics based approach that can be used to analyze and improve web usage pattern. We define a set of 15 web metrics that can play an important role in understanding web visitors behavior and provide suggestion how these metrics can help in making a website more popular. We describe the approach by considering a case study of the website gndu.ac.in for the data collected over a period of five months.

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16

BASCI, D., and S. MISRA. "Data Complexity Metrics for XML Web Services." Advances in Electrical and Computer Engineering 9, no. 2 (2009): 9–15. http://dx.doi.org/10.4316/aece.2009.02002.

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17

B.Senousy, M., and Anthony Nosshi Shaker. "Experimental & Analytical Evaluation of Web Metrics." مجلة الجمعیة المصریة لنظم المعلومات وتکنولوجیا الحاسبات 12, no. 12 (January 1, 2012): 4–13. http://dx.doi.org/10.21608/jstc.2012.120070.

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18

Jayanthi, J., M. Ezhilmathi, and S. Rathi. "Evaluating the Effectiveness of Web Search Metrics." International Journal of Applied Information Systems 4, no. 10 (December 15, 2012): 28–35. http://dx.doi.org/10.5120/ijais12-450818.

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19

Brogan, Mark. "Web Metrics for Library and Information Professionals." Archives and Manuscripts 43, no. 1 (January 2, 2015): 74–76. http://dx.doi.org/10.1080/01576895.2014.1000808.

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20

Palmer, Jonathan W. "Web Site Usability, Design, and Performance Metrics." Information Systems Research 13, no. 2 (June 2002): 151–67. http://dx.doi.org/10.1287/isre.13.2.151.88.

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21

Kruesi, Lisa. "Web Metrics for Library and Information Professionals." Australian Academic & Research Libraries 46, no. 2 (April 3, 2015): 143–44. http://dx.doi.org/10.1080/00048623.2015.1031627.

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22

Pooley, R., D. Senior, and D. Christie. "Collecting and analyzing Web-based project metrics." IEEE Software 19, no. 1 (2002): 52–58. http://dx.doi.org/10.1109/52.976941.

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23

Liao, Lili, Guohua Shen, Zhiqiu Huang, and Fei Wang. "Cohesion Metrics for Evaluating Semantic Web Ontologies." International Journal of Hybrid Information Technology 9, no. 11 (November 30, 2016): 369–80. http://dx.doi.org/10.14257/ijhit.2016.9.11.31.

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24

Mendes, E., N. Mosley, and S. Counsell. "Web metrics - estimating design and authoring effort." IEEE Multimedia 8, no. 1 (2001): 50–57. http://dx.doi.org/10.1109/93.923953.

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25

Jaillet, Hélène F. "Web metrics: measuring patterns in online shopping." Journal of Consumer Behaviour 2, no. 4 (June 2003): 369–81. http://dx.doi.org/10.1002/cb.114.

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26

Lamot, Kenza, Steve Paulussen, and Peter Van Aelst. "Do Metrics Drive News Decisions? Political News Journalists’ Exposure and Attitudes Toward Web Analytics." Electronic News 15, no. 1-2 (March 2021): 3–20. http://dx.doi.org/10.1177/19312431211018141.

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As newsrooms are increasingly using web analytics to monitor news behavior, journalism is likely to become increasingly “metrics-driven.” Research suggests that analytics are commonly used by web editors to decide on the distribution and promotion of news stories, but how does this affect the news practices of journalists? To what extent are audience metrics taken into account by individual journalists and reporters who work in a specific news beat? This paper explores this question through a survey of political journalists in Belgium. The study examines the level of access that political news journalists have to audience metrics, and to what extent their level of exposure to and use of metrics affects their attitudes toward analytics in news work. Results show that while three quarters of the political news journalists are nowadays exposed to audience metrics on a regular basis, more than half of them report to never make direct use of web metrics in their daily work. Younger journalists are more likely to be exposed and to use web metrics than their senior colleagues, but journalists working for commercial media do not use metrics more intensely than journalists from public service media. Journalists who actively use metrics themselves tend to hold more positive attitudes toward web metrics, whereas the passive exposure to metrics seems to make journalists more skeptical or negative about them.

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27

Bora, Abhijit, and Tulshi Bezboruah. "Some Aspects of Implementation of Web Services in Load Balancing Cluster-Based Web Server." International Journal of Information Retrieval Research 10, no. 1 (January 2020): 48–72. http://dx.doi.org/10.4018/ijirr.2020010104.

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We have designed, developed, and implemented SOAP-based web services in load balancing cluster-based web server and carried out load testing over the system. The roles of web services, such as client, broker, and service provider are segregated among different services. The system is monitored through a load testing tool, Mercury LoadRunner. The recorded system metrics are evaluated to study the overall performance and reliability aspects against different massive level of users. This article presents in detail the system architecture, testing methodology, and recorded system metrics. The statistical analysis is carried out to validate and correlate the overall assessment. This article also provides insights of some aspects of system metrics for deploying web services with segregated roles by using a cluster-based web server. It is observed that service with segregated roles is better than the service with merged roles. As a result, performance and reliability of the proposed system is observed to be better than other generic techniques for such deployment.

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28

Daugherty, Alan, Robert A. Hegele, Hong S. Lu, Nigel Mackman, Daniel J. Rader, and Christian Weber. "Web of Science’s Citation Median Metrics Overcome the Major Constraints of the Journal Impact Factor." Arteriosclerosis, Thrombosis, and Vascular Biology 42, no. 4 (April 2022): 367–71. http://dx.doi.org/10.1161/atvbaha.122.317426.

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There are many metrics to evaluate the performance and status of journals. Among these, the journal impact factor (JIF) has become the dominant metric. The influence of JIF is illustrated by its widespread use to evaluate academic status, compensation, and funding decisions. However, as noted by Clarivate Analytics, the parent company of the Web of Science (WoS), the JIF should not be used without careful attention to the many phenomena that influence citation rates. To facilitate transparency, Clarivate Analytics provides all data used to determine the JIF. In addition, WoS provides other metrics for journal evaluation, including the article citation median and the review citation median. These metrics are represented as medians to minimize the confounding influence of a small number of highly cited articles that may occur when data are represented as means. Another feature of these WoS metrics is that data are separated according to different publication types of article (original research and review). To systematically compare these selected metrics, we used the data provided on the WoS web site to analyze 25 top ranked cardiovascular journals in the same mode as represented in the WoS citation distribution window. The results indicate that the article citation median and review citation median overcome several concerns that have been raised about the JIF and seem to provide enhanced objectivity as an indicator of journal impact in publishing original research and reviews. Therefore, we advocate that these additional WoS metrics might be preferentially considered as indicators of journal performance.

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29

Bekavac, Ivan, and Daniela Garbin Praničević. "Web analytics tools and web metrics tools: An overview and comparative analysis." Croatian Operational Research Review 6, no. 2 (October 31, 2015): 373–86. http://dx.doi.org/10.17535/crorr.2015.0029.

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30

Malhotra, Ruchika, and Anjali Sharma. "Quantitative evaluation of web metrics for automatic genre classification of web pages." International Journal of System Assurance Engineering and Management 8, S2 (May 27, 2017): 1567–79. http://dx.doi.org/10.1007/s13198-017-0629-1.

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31

Wang, Juan, Xinyue Qi, Bo Cui, and Maozu Guo. "A Survey of Metrics Measuring Difference for Rooted Phylogenetic Trees." Current Bioinformatics 15, no. 7 (December 15, 2020): 697–702. http://dx.doi.org/10.2174/1574893614666191017130217.

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Background: The evolutionary history of organisms can be described by phylogenetic trees. We need to compare the topologies of rooted phylogenetic trees when researching the evolution of a given set of species. Objective: Up to now, there are several metrics measuring the dissimilarity between rooted phylogenetic trees, and those metrics are defined by different ways. Methods: This paper analyzes those metrics from their definitions and the distance values computed by those metrics by terms of experiments. Results: The results of experiments show that the distances calculated by the cluster metric, the partition metric, and the equivalent metric have a good Gaussian fitting, and the equivalent metric can describe the difference between trees better than the others. Conclusion: Moreover, it presents a tool called as CDRPT (Computing Distance for Rooted Phylogenetic Trees). CDRPT is a web server to calculate the distance for trees by an on-line way. CDRPT can also be off-line used by means of installing application packages for the Windows system. It greatly facilitates the use of researchers. The home page of CDRPT is http://bioinformatics.imu.edu.cn/tree/.

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32

Delgado-López-Cózar, Emilio, and Rafael Repiso-Caballero. "The impact of scientific journals of communication: Comparing Google Scholar Metrics, Web of Science and Scopus." Comunicar 21, no. 41 (June 1, 2013): 45–52. http://dx.doi.org/10.3916/c41-2013-04.

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Google Scholar Metrics' launch in April 2012, a new bibliometric tool for the evaluation of scientific journals by means of citation counting, has ended with the duopoly exerted by the Web of Science and Scopus databases. This paper aims at comparing the coverage of these three databases and the similarity their journal rankings may have. We selected a sample of journals from the field of Communication Studies indexed in the three databases. Data was recollected on 1720 November, 2012. 277 journals were identified to which we calculated their hindex and ranked them according to such indicator. Then, we analyzed the correlation between the rankings generated. Google Scholar Metrics dobles the coverage of the other databases, reducing the bias toward English language both; web of Science and Scopus have. Google Scholar Metrics shows higher hindex values (an average 47% higher than Scopus and 40% higher than Web of Science), allowing to better rank journals. We conclude that Google Scholar Metrics is a tool capable of identifying the main journals in Communication Studies offering results as reliable and valid as the ones Web of Science and Scopus show. La aparición de Google Scholar Metrics en abril de 2012 como nuevo sistema de evaluación bibliométrica de revistas científicas a partir del recuento de las citas bibliográficas que éstas han recibido en Google Scholar rompe el duopolio ejercido hasta el momento por las bases de datos Web of Science y Scopus. El objetivo de este trabajo es comparar la cobertura que poseen estas tres bases de datos y la similitud que puedan presentar los rankings elaborados a partir de ellas. Se ha elegido como muestra las revistas de comunicación indizadas en las tres bases de datos. Las búsquedas bibliográficas se efectuaron entre el 17 y el 20 de noviembre de 2012. Se calcula el índice h de las 277 revistas identificadas y se averigua la correlación existente entre los rankings generados. Google Scholar Metrics duplica la cobertura, reduce el sesgo anglosajón que poseen Web of Science y Scopus. Google Scholar Metrics proporciona índices h más elevados (un promedio de un 47% superior a Scopus y un 40% a Web of Science) con lo que permite discriminar mejor las posiciones de las revistas en el ranking. En conclusión, Google Scholar Metrics es una herramienta capaz de identificar las principales revistas de comunicación ofreciendo resultados tan solventes, fiables y válidos como los generados por Web of Science y Scopus.

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33

Vu, Hong Tien. "The online audience as gatekeeper: The influence of reader metrics on news editorial selection." Journalism 15, no. 8 (October 28, 2013): 1094–110. http://dx.doi.org/10.1177/1464884913504259.

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This study provides a snapshot of the hierarchy-of-influences model in the new media environment through examining the effects of audience web metrics on editors. Surveying 318 gatekeepers, the study found that audience metrics influence editors in gatekeeping. Editors’ likelihood to monitor web metrics is affected by their journalism training. Gatekeepers who attach the importance of high readership to economic benefits are more likely to have different news decisions based on web metrics. The study suggests a revision of the hierarchy-of-influences model with more emphasis being placed on the role of the audience.

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34

Bezboruah, Tulshi, and Abhijit Bora. "Performance Evaluation of Hierarchical SOAP Based Web Service in Load Balancing Cluster-Based and Non-Cluster-Based Web Server." International Journal of Information Retrieval Research 5, no. 4 (October 2015): 19–30. http://dx.doi.org/10.4018/ijirr.2015100102.

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Evaluating the implementation techniques of web service and analysing their performance in load balancing cluster-based and non-cluster-based web server is necessary from the perspective of web service developer, researchers as well as users. As such the authors propose to develop and implement a SOAP based hierarchical web service using load balancing cluster-based and non-cluster based Apache Tomcat web server to study the web service performance metrics. The performance of overall system is tested using the load testing tool Mercury Load runner and a comparative investigation is carried out using the stability, load and performance metrics of both services. In this paper, the authors present, in detail, the methodology of experiment, comparative testing results, and statistical analysis on performance metrics.

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35

ZENG, Yi, Xiao-wei HU, and Juan LI. "Web software complexity metrics based on projection pursuit." Journal of Computer Applications 32, no. 3 (April 2, 2013): 827–30. http://dx.doi.org/10.3724/sp.j.1087.2012.00827.

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36

Tripathi, Sandesh, Abbas, and Rizwan Beg. "Availability metrics: under controlled environments for web services." International Journal on Web Service Computing 2, no. 3 (September 30, 2011): 1–9. http://dx.doi.org/10.5121/ijwsc.2011.2301.

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37

Davlatov, Sh R., and P. V. Kuchinsky,. "Web resource security analysis based on CVSS metrics." Informatics 17, no. 3 (September 30, 2020): 72–77. http://dx.doi.org/10.37661/1816-0301-2020-17-3-72-77.

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Based on the analysis of vulnerability data for web resources and the CVSS metric, the distribution of the average CVSS (Common Vulnerability Scoring System standard for calculating a numerical vulnerability score on a ten-point scale) score for the websites of theRepublicofBelaruswas studied. The hypothesis on the distribution of the CVSS vulnerability assessment according to Poisson's law was tested by chi-square criteria. It was found that about 10% of web resources from the original general of samples of 19000 size have a critical averaged assessment level of vulnerability. As part of this work an universal system for collecting technical information about active web resources on the Internet from public directories and registries has been developed. Specific search templates have been developed using RegExp JavaScript expressions to detect the versions of technologies that were used to create websites. Based on this data the percentage distribution of used technologies, top-level domains and the geographical location of the servers were calculated. Proposed system can be adapted to any unique conditions required by information security specialists to conduct a security audit of web resources.

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38

Layton, Astrid, Bert Bras, and Marc Weissburg. "Designing Industrial Networks Using Ecological Food Web Metrics." Environmental Science & Technology 50, no. 20 (September 29, 2016): 11243–52. http://dx.doi.org/10.1021/acs.est.6b03066.

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39

Koutlis, Christos, Manos Schinas, Symeon Papadopoulos, and Ioannis Kompatsiaris. "GAP: Geometric Aggregation of Popularity Metrics." Information 11, no. 6 (June 15, 2020): 323. http://dx.doi.org/10.3390/info11060323.

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Estimating and analyzing the popularity of an entity is an important task for professionals in several areas, e.g., music, social media, and cinema. Furthermore, the ample availability of online data should enhance our insights into the collective consumer behavior. However, effectively modeling popularity and integrating diverse data sources are very challenging problems with no consensus on the optimal approach to tackle them. To this end, we propose a non-linear method for popularity metric aggregation based on geometrical shapes derived from the individual metrics’ values, termed Geometric Aggregation of Popularity metrics (GAP). In this work, we particularly focus on the estimation of artist popularity by aggregating web-based artist popularity metrics. Finally, even though the most natural choice for metric aggregation would be a linear model, our approach leads to stronger rank correlation and non-linear correlation scores compared to linear aggregation schemes. More precisely, our approach outperforms the simple average method in five out of seven evaluation measures.

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40

Liang, Qianhui, Anandhi Bharadwaj, and Bu Sung Lee. "Interactive and Iterative Service-Composition-Based Approach to Flexible Information System Development." International Journal of Web Services Research 8, no. 4 (October 2011): 81–107. http://dx.doi.org/10.4018/jwsr.2011100104.

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An emerging class of technologies defined as Service-Oriented Architecture (SOA) has been heralded as the answer for inflexible IT architecture and promises to reduce operational barriers of current IT infrastructures. In SOA, loosely coupled Web services are integrated to provide dynamic digital capabilities within and across enterprise boundaries. Little research exists on development processes of information systems using Web services and against certain development metrics. One way to perform such research is to propose a development approach, identify the metrics, and embed the metrics into the technique of service composition to allow system development with desired characteristics. This paper reports an approach to information system development based on Web services composition and the metrics designed for such approaches. This approach is based on semi-automatic, interactive, and iterative Web service composition -- a hybrid technique based on developing and searching an AND/OR graph for composite services discovery while taking into consideration human judgment for solution selection and validation by interactions in an iterative way. The composition process leverages historical Web service usage data and provides helpful suggestions to the users regarding available component services. The authors propose that the metrics can investigate the characteristics of such development approaches.

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41

Cho, Jae Han, Jae Min Park, Tae Hyeop Kim, Seung Wook Lee, and Jiyeon Kim. "Machine Learning-based Detection of HTTP DoS Attacks for Cloud Web Applications." Korean Institute of Smart Media 12, no. 2 (March 30, 2023): 66–75. http://dx.doi.org/10.30693/smj.2023.12.2.66.

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Recently, the number of cloud web applications is increasing owing to the accelerated migration of enterprises and public sector information systems to the cloud. Traditional network attacks on cloud web applications are characterized by Denial of Service (DoS) attacks, which consume network resources with a large number of packets. However, HTTP DoS attacks, which consume application resources, are also increasing recently; as such, developing security technologies to prevent them is necessary. In particular, since low-bandwidth HTTP DoS attacks do not consume network resources, they are difficult to identify using traditional security solutions that monitor network metrics. In this paper, we propose a new detection model for detecting HTTP DoS attacks on cloud web applications by collecting the application metrics of web servers and learning them using machine learning. We collected 18 types of application metrics from an Apache web server and used five machine learning and two deep learning models to train the collected data. Further, we confirmed the superiority of the application metrics-based machine learning model by collecting and training 6 additional network metrics and comparing their performance with the proposed models. Among HTTP DoS attacks, we injected the RUDY and HULK attacks, which are low- and high-bandwidth attacks, respectively. As a result of detecting these two attacks using the proposed model, we found out that the F1 scores of the application metrics-based machine learning model were about 0.3 and 0.1 higher than that of the network metrics-based model, respectively.

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42

Balaji, B., and M. Dhanamjaya. "Preprints in Scholarly Communication: Re-Imagining Metrics and Infrastructures." Publications 7, no. 1 (January 14, 2019): 6. http://dx.doi.org/10.3390/publications7010006.

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Digital scholarship and electronic publishing within scholarly communities change when metrics and open infrastructures take center stage for measuring research impact. In scholarly communication, the growth of preprint repositories as a new model of scholarly publishing over the last three decades has been one of the major developments. As it unfolds, the landscape of scholarly communication is transitioning—with much being privatized as it is made open—and turning towards alternative metrics, such as social media attention, author-level, and article-level metrics. Moreover, the granularity of evaluating research impact through new metrics and social media changes the objective standards of evaluating research performance. Using preprint repositories as a case study, this article situates them in a scholarly web, examining their salient features, benefits, and futures. Moves towards scholarly web development and publishing on the semantic and social web with open infrastructures, citations, and alternative metrics—how preprints advance building the web as data—is discussed. We determine that this will viably demonstrate new metrics and, by enhancing research publishing tools in the scholarly commons, facilitate various communities of practice. However, for preprint repositories to be sustainable, scholarly communities and funding agencies should support continued investment in open knowledge, alternative metrics development, and open infrastructures in scholarly publishing.

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43

Burghardt, Kyle J., Bradley H. Howlett, Audrey S. Khoury, Stephanie M. Fern, and Paul R. Burghardt. "Three Commonly Utilized Scholarly Databases and a Social Network Site Provide Different, But Related, Metrics of Pharmacy Faculty Publication." Publications 8, no. 2 (April 1, 2020): 18. http://dx.doi.org/10.3390/publications8020018.

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Scholarly productivity is a critical component of pharmacy faculty effort and is used for promotion and tenure decisions. Several databases are available to measure scholarly productivity; however, comparisons amongst these databases are lacking for pharmacy faculty. The objective of this work was to compare scholarly metrics from three commonly utilized databases and a social networking site focused on data from research-intensive colleges of pharmacy and to identify factors associated with database differences. Scholarly metrics were obtained from Scopus, Web of Science, Google Scholar, and ResearchGate for faculty from research-intensive (Carnegie Rated R1, R2, or special focus) United States pharmacy schools with at least two million USD in funding from the National Institutes of Health. Metrics were compared and correlations were performed. Regression analyses were utilized to identify factors associated with database differences. Significant differences in scholarly metric values were observed between databases despite the high correlations, suggestive of systematic variation in database reporting. Time since first publication was the most common factor that was associated with database differences. Google Scholar tended to have higher metrics than all other databases, while Web of Science had lower metrics relative to other databases. Differences in reported metrics between databases are apparent, which may be attributable to the time since first publication and database coverage of pharmacy-specific journals. These differences should be considered by faculty, reviewers, and administrative staff when evaluating scholarly performance.

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Scott, S. D., and Y. H. Koh. "Design Metrics and the Adaptation of Web-Page Content Chunks for PDAs." Journal of IT in Asia 1, no. 1 (July 21, 2017): 35–51. http://dx.doi.org/10.33736/jita.404.2005.

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The majority of web-pages are unsuitable for viewing on PDAs, WAP phones and similar devices without first being adapted. However, little empirical work has been done on what actually constitutes a good PDA or WAP web-page. This paper ranks a number of PDA web-pages from different categories empirically and correlates the result against the design metrics present. The findings are then compared against a similar set of experiments for PC web-pages. The results of this comparison suggest that, as well as omitting, summarizing and converting individual multimedia objects in the web-page to a less resource intensive form, the design metrics need to be changed during adaptation to enhance the presentation of web-content on non-PC devices. The paper concludes by investigating the effect of applying some suitable changes to the design metrics on web=page content chunks, which form the basic units in automatic content adaptation systems.

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Udartseva, O. M., and A. E. Rykhtorova. "Using web analytics tools to assess the effectiveness of means for promoting library resources." Bibliosphere, no. 2 (June 30, 2018): 93–99. http://dx.doi.org/10.20913/1815-3186-2018-2-93-99.

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This article is devoted to analysis of the technologies effectiveness to promote library resources. The first part explores using web analytics and its tools in the library. The study of existing approaches to choose web metrics allows identifying the following key metrics to assess the effectiveness of the library web site operation: (1) attendance metrics; (2) the metrics of involvement and loyalty; (3) social metrics. The techniques effectiveness direct evaluation is proposed to promote library resources based on the analysis of: (1) measurable targeted actions by users on the site; (2) transitions to the library site by external links from search engines; (3) sites with high density of clicks and conversion of site landing pages; (4) existing segments of users and their activity. This paper provides perspectives showing what ways this emerging area of study can help in understanding and improving the perception of products and services on websites of libraries.

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Dooleya, Jennifer Allyson, Don Iversona, and Sandra C. Jonesa. "Are There Good Metrics for Evaluating Web 2.0 Campaigns?" Journal of Social Media Studies 1, no. 2 (January 20, 2015): 83–90. http://dx.doi.org/10.15340/2147336612783.

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Selvi, R. Thirumalai, Adham Sheriff A, George T. Manohar, and Balasubramanian N.V. "Problem and Solution Oriented Metrics for Web Based Applications." i-manager's Journal on Software Engineering 2, no. 1 (September 15, 2007): 1–5. http://dx.doi.org/10.26634/jse.2.1.556.

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John, Biju, Souheil Khaddaj, and Andreas Hoppe. "Software Metrics for the Evaluation of Semantic Web Applications." Journal of Algorithms & Computational Technology 4, no. 4 (December 2010): 511–22. http://dx.doi.org/10.1260/1748-3018.4.4.511.

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Dikaiakos, Marios D., Athena Stassopoulou, and Loizos Papageorgiou. "An investigation of web crawler behavior: characterization and metrics." Computer Communications 28, no. 8 (May 2005): 880–97. http://dx.doi.org/10.1016/j.comcom.2005.01.003.

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Malhotra, Ruchika, and Anjali Sharma. "Estimating the threshold of software metrics for web applications." International Journal of System Assurance Engineering and Management 10, no. 1 (February 2019): 110–25. http://dx.doi.org/10.1007/s13198-019-00773-1.

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